A comparison of our method and previous approaches is listed in Table REF . We report our result with mean instance IoU. We train three versions of our method, a compact network with \(0.5\times C\)  feature channels, a medium size network with \(0.75\times C\)  feature channels, and a full size network. Our compact network is able to achieve the same results as PointNet++[1]}, with \(2\times \)  speedup, and 0.7G less memory consumption. Comparing with DGCNN our compact method is \(2\times \)  faster and only half of its memory usage. Comparing with PV CNN[2]}, our method achieves comparable results.
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